Research and Analysis of Dark Channel Priori Dehazing Algorithm based on Guided Filtering

Abstract

The dark channel priori dehaze algorithm based on minimum filtering is known to consume a significant amount of computational and storage resources for transmittance optimization, resulting in issues such as halo phenomena in gray and white areas of the image. In contrast to this, the proposed algorithm in this paper offers a novel approach to dark channel image dehazing. By leveraging dark channel a priori knowledge, the algorithm introduces an adaptive adjustment factor to enhance the realism of restored image details. Furthermore, the algorithm employs guided filtering for transmittance map refinement instead of traditional image keying. Subsequently, the haze-free image is reconstructed using the estimated atmospheric light and refined transmittance maps based on the atmospheric scattering model. Post image restoration, brightness and contrast are enhanced, and image optimization is achieved through adaptive contrast histogram equalization to improve visual quality. The experimental findings reveal that the proposed algorithm not only accelerates the efficiency of image dehazing but also sustains color fidelity in gray and white regions, yielding aesthetically pleasing outcomes.